Method and system for personalized recommendation modeling

ABSTRACT

A method and system for modeling the interests of an individual television viewer based on logging the user&#39;s television usage activities for the purpose of determining personalized recommendations is presented. According to one aspect of the claimed subject matter, user input in response to a questionnaire, the user&#39;s recorded television usage, the user&#39;s previous interactions with the personalized recommendations and contextual data such as external events and social group influences are used to model the probability of a user&#39;s interest in available media content. Data from the model is subsequently arranged to display units of available media content with the highest probabilities of user interest to the user. According to further embodiments, specific user behavior in previously consumed media content such as a user&#39;s selection, a user&#39;s continuous consumption, and a user&#39;s discontinuation of consumption is tracked and used as input.

BACKGROUND

Recently, the proliferation and popularity of media content deliveredover telecommunication networks and available to viewers has drasticallyincreased. The ability to stream large collections of media content frommultiple sources (such as cable television, satellite television,Internet Video-On-Demand services, home media centers, etc.) onto atelevision has led to an excess of available viewing material and aresulting surfeit of viewing choices available to a viewer. This canlead to the traditional practice of “channel “surfing”—that is,sequentially (or even randomly) traversing a series of streamingtelevision channels—to further include traversing lists of recordedtelevision and Video-On-Demand (“VOD”) offerings. This behavior can leadto inefficiency and user frustration, due to potentially missing acritical point in a television program or lengthy delays betweenprograms of user interest. Unfortunately, existing methods forpersonalizing television programming via program recommendations fail tocapture individual user preferences as this requires the need forlogging individual television usage history. Collecting individualtelevision usage history can be a very challenging task and typically,most efforts at logging television usage history have been performed byrequesting the user to manually enter the programs the user has watched.However, not only is this usually insufficient to comprehensively modelthe user's interests, the process can also be inaccurate (due to smallsample sizes) and/or burdensome on the user to manually maintain suchlogs.

In contrast, web personalization by mining usage logs for modeling auser's personal taste has been widely applied in personalization of websites. Web personalization is the process of customizing a web siteaccording to the needs or preferences of specific users by leveragingthe knowledge acquired from the analysis of the user's recordednavigational behavior (usage data) in correlation with other informationcollected in the Web context, namely structure, content and user profiledata.

Web site personalization is mostly performed at the web site server.This makes maintaining consistent aggregate individual logs difficult tocollect and manage as the user can access the site from differentmachines, each with different Internet Protocol (“IP”) addresses andMedia Access Control (“MAC”) addresses. As a solution to this problem,certain websites require the user to create and maintain personalaccounts on their sites to benefit from their personalized services.However, as the web site usage logs are typically stored at the server,this can raise privacy issues and the usage of which may be undesirablefor a user. Furthermore, even though such web sites can offer some levelof personalization, it cannot be done across all websites as differentwebsites may have different content management, authentication, andcustomization techniques. Naturally, this also serves as an obstacle toapplying website personalization tools to television viewing, as thesesame personalization tools would not be able to be directly applied tousage logs of television viewing from multiple sources and contentproviders.

Moreover, usage statistics and patterns mined from web logs would besignificantly different from television usage logs as the nature ofcontent viewed on television can vary—viz, linear (scheduled content),Video On Demand (“VOD”), Live, Previously Recorded Content, etc.,whereas web content is almost entirely at least capable of beingdelivered on demand. For example, linear television show programs areaired according to a schedule and the user may not have watched thecontent from the beginning, or the user's viewing progress may not be upto date with the program's current airing. The user's actual interest inthe program then, can be significantly different from that of a VODshow, even though the user may have consistently watched both for sometime.

As a further distinction, television streams, even though from multiplesources, have a predefined presentation format (e.g.,scheduled/VOD/Live, etc.) and meta-data (e.g., series/synopsis/duration,etc.) For television content, the content provider has complete controlover how, when and what is shown which facilitates the activity flow tobe traceable to help identify personalization traits. Web content,however, is mostly available on-demand and hence with webpersonalization tools observing time based viewing behavior oftelevision shows may be incomplete. Due to these distinctions, simplyapplying web-customization tools to television usage would not prove tobe an effective means to provide personalized management of televisionconsumption.

SUMMARY

As a solution to the type of problems noted above, this disclosureprovides novel methods and systems for modeling a user's viewing profileacross multiple sources and content providers.

In an embodiment, a method is provided which allows the dynamicgeneration of a personalized schedule of recommend programs of availablemedia content. By generating such a personalized schedule, a user isprovided with a listing of units of available content (e.g., episodes,live sporting events, television programs) likely to be of interest tothe user, thereby largely eliminating the need for channel surfing orother inefficient user behavior to find a program of interest. Bydynamically generating a personalized schedule, the user's recentactivity, interests, and mood may be reflected so as to provide alisting with greater accuracy and/or timeliness.

According to further embodiments, pre-submitted user input in responseto a questionnaire, the user's recorded television usage, the user'sprevious interactions with the personalized schedule, and contextualdata such as external events and social group influences are used tomodel the probability of a user's interest in available media content.Data output from the model is subsequently arranged to display units ofavailable media content with the highest probabilities of user interestto the user. According to still further embodiments, specific userbehavior in previously consumed media content such as a user'sselection, a user's consumption, and a user's discontinuation ofconsumption is tracked, analyzed and used as input.

According to another aspect of the claimed subject matter, a system isprovided to implement and execute the method described above. Thissystem includes a display device coupled to a plurality of media contentsources (such as a cable or satellite television box, a home mediacenter, etc.) and configured to track a user's viewing history andbehavior during television usage. A corresponding computing device, suchas a remote computing device, may be subsequently used to display adynamically generated schedule of recommended viewing choices customizedfor the user. This remote computing device may be implemented as, forexample, a television remote control, smart-phone, laptop computer,tablet, etc. In still further embodiments, a graphical user interface ofthe remote computing device may display the dynamically generatedschedule, as well as receive input from the user through the interface.This input may be tracked and recorded and used as input to generateadditional customized schedules.

According to various embodiments, the method described above may beimplemented as programmed instructions stored on a computer readablemedium, such as memory in a computing device. As a result of the systemsand methods described herein, a personalized schedule of recommendedcontent from multiple content sources may be generated for a user basedon a data model of the user's viewing profile to assist the user inmaking adaptive, informed, and positive viewing decisions. As hereindescribed, a “user” may refer to a single individual, or a collection ofassociated individuals (such as members of a household, a family, abusiness, a social group, etc.) According to these implementations,while each member of the collective may have a corresponding personalmodel and separate personalized schedule of recommended content, thecollective association itself may also have a customized model andcorresponding customized schedule that coexist which cocxicts with themodels and schedules of the individual members. When conflicts orinconsistencies arise between generated schedules, system rules and/orpolicies may be applied in these units to determine the priority of thecollective “user.” For example, if the presence of two or moreindividuals of a collective association is simultaneously detected, themodel of the collective takes precedence. Alternate policy or rulesystems may be designed and implemented according to conventional means.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and form a part ofthis specification, illustrate embodiments of the disclosure and,together with the description, serve to explain the principles of thepresently claimed subject matter:

FIG. 1 depicts a data flow diagram of a system for generating apersonalized media content schedule for a user, in accordance withembodiments of the present disclosure.

FIG. 2 depicts a flowchart of a process for providing a personalizedmedia content schedule for a user, in accordance with embodiments of thepresent disclosure.

FIG. 3 depicts a flowchart of a process for dynamically generating aschedule of recommended available media content, in accordance withembodiments of the present disclosure.

FIG. 4 depicts a data flow diagram of a process for performing usage loganalysis, in accordance with embodiments of the present disclosure.

FIG. 5 depicts a list of exemplary reasons contributing to logged userbehavior, in accordance with embodiments of the present disclosure.

FIG. 6 depicts a sample questionnaire to query a user for user-input, inaccordance with embodiments of the present disclosure.

FIG. 7 is a depiction of an exemplary system for generating apersonalized media content schedule for a user, in accordance withembodiments of the present disclosure.

FIG. 8 depicts an exemplary graphical user interface of a dynamicallygenerated schedule of recommended available media content, in accordancewith embodiments of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to several embodiments. While thesubject matter will be described in conjunction with the alternativeembodiments, it will be understood that they are not intended to limitthe claimed subject matter to these embodiments. On the contrary, theclaimed subject matter is intended to cover alternative, modifications,and equivalents, which may be included within the spirit and scope ofthe claimed subject matter as defined by the appended claims.

Furthermore, in the following detailed description, numerous specificdetails are set forth in order to provide a thorough understanding ofthe claimed subject matter. However, it will be recognized by oneskilled in the art that embodiments may be practiced without thesespecific details or with equivalents thereof. In other instances,well-known processes, procedures, components, and circuits have not beendescribed in detail as not to unnecessarily obscure aspects and featuresof the subject matter.

Portions of the detailed description that follow are presented anddiscussed in terms of a process. Although operations and sequencingthereof are disclosed in a figure herein (e.g., FIGS. 2 and 3)describing the operations of this process, such operations andsequencing are exemplary. Embodiments are well suited to performingvarious other operations or variations of the operations recited in theflowchart of the figure herein, and in a sequence other than thatdepicted and described herein.

Some portions of the detailed description are presented in terms ofprocedures, operations, logic blocks, processing, and other symbolicrepresentations of operations on data bits that can be performed oncomputer memory. These descriptions and representations are the meansused by those skilled in the data processing arts to most effectivelyconvey the substance of their work to others skilled in the art. Aprocedure, computer-executed operation, logic block, process, etc., ishere, and generally, conceived to be a self-consistent sequence ofoperations or instructions leading to a desired result. The operationsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared, and otherwise manipulated in a computer system. Ithas proven convenient at times, principally for reasons of common usage,to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout, discussions utilizingterms such as “accessing,” “writing,” “including,” “storing,”“transmitting,” “traversing,” “associating,” “identifying” or the like,refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

While the following example configurations are shown as incorporatingspecific, enumerated features and elements, it is understood that suchdepiction is exemplary. Accordingly, embodiments are well suited toapplications involving different, additional, or fewer elements,features, or arrangements.

Generating Recommended Schedules

FIG. 1 depicts a data flow diagram 100 of a system for generating apersonalized media content schedule for a user, in accordance withembodiments of the present disclosure. In particular, FIG. 1 depicts thecomponents involved during a user modeling and content recommendationprocess.

As depicted in FIG. 1, an adaptive recommendation process is applied toa generated user model to dynamically create a customized program guidewith recommendations of available media content for a user. As shown, auser model 107 may take up to two user dependent inputs: a set 101 ofuser responses to a list of optional preference questions and a remoteusage log 103. According to an embodiment, the list of preferencequestions may comprise a set of single answer or rating-based questionsthat the user may answer as an initialization step during registrationand used as input when creating the user's personal profile. Thequestions presented to the user may be designed to correspond to generalabstract classifications and/or attributes of interest to the user,etc., such that a user's response will provide an indication of theuser's particular preference among those interests. Alternatively, whenthe user elects not to respond to all or a portion of the preferencequestions, one or more default responses may be automatically enteredfor the user. In further embodiments, the default response may beautomatically selected for the user based on the user-related context,such as the averaged response for social or peer groups associated withthe user, and local current events corresponding to the user.

The remote usage log 103 records the usage activities performed whichcorrespond to the display of content in a display device and viewed bythe user. These usage activities may include, for example, userinteractions with a remote device which selects, deselects, or changes aunit of content consumed, and user interaction with a personalizedcontent schedule generated by the system and presented to the user. Theremote usage log is further processed by analyzing the usage log 105 todetermine correlations between instances of user behavior—such as a userselection, consumption, and discontinuation of consuming—in connectionwith of watched programs and distilling the particular reasons which maycontribute to the user's behavior, and mapping the correlations to unitsof available media content.

A user model 107 can be generated with the inputs from the user loganalysis 103, and the user input 101 to predict a user's level ofinterest in units of available media content. In further embodiments,the user model 107 can also take as input meta-data corresponding to thecontent consumed (i.e., watched) by the user. In still furtherembodiments, the user model 107 can also consider additional user-basedcontextual information (e.g., knowledge of external events andinformation about interests of other users in social groups to which theuser belongs). In one embodiment, each input in the user model may bemapped to a unit of available media content by the content's meta-data,and expressed as a value corresponding to the particular input. Forexample, the user's expressed (or defaulted) preferences in abstractclassifications may be mapped to units of available media content withmeta-data which corresponds to the classifications and expressed as avalue. Likewise, user behavior indicative of a user's interest (e.g.,selecting and watching a program) in previously consumed units of mediacontent can be mapped to units of available media content and expressedas another value. Previous interactions with a personalized schedule,external events and social group influences may also be analyzed, datamined for user preferences, mapped to the units of available mediacontent, and expressed as a value indicative of the user's interest inany of the units of available content. The user's interest in a unit ofavailable media content then may be determined by aggregating the valuesof the mapped inputs of the user model 107 for the unit into a userinterest score, for example. These scores may be compiled into a userinterest list, which matches the user interest score with the unit ofavailable media content.

An Adaptive Recommendation Process 109 is then applied to parse the listof user interest list to determine the units with the highest calculateduser interest score. A smaller list of the units of available mediacontent with the highest calculated user interest scores may be arrangedaccording to a schedule and graphically displayed to the user as apersonalized schedule. In an embodiment, the output of therecommendation system is a set or graphical list of shows and programs(VOD, live, recorded, and linear content) presented in the form of apersonalized electronic program guide (pEPG) 111 and arranged by theuser's predicted interest levels. Interactions with this guide are alsoindicative of the quality of recommendations provided by the system.These interactions are also fed back as part of the remote usage log tofurther improve and refine recommendations provided by the system.

With reference now to FIG. 2, a flowchart of a process 200 for providinga personalized media content schedule for a user is depicted, inaccordance with embodiments of the present disclosure. Steps 201-207describe exemplary steps comprising the process 200 depicted in FIG. 2in accordance with the various embodiments herein described. In oneembodiment, the process 200 is implemented in whole or in part ascomputer-executable instructions stored in a computer-readable mediumand executed in a computing device.

At step 201, one or more media content sources are referenced todetermine available media content. Available media content may consistof, for example, units of content such as movies, television programs,live sports or performances, recorded home videos, etc, that areaccessible to the user from a service provider (through user agreements,service plans, licenses, personal ownership, etc.). At step 203, theunits of available media content determined in step 201 are evaluatedand a customized, recommended schedule of those units is generated forthe user. According to an embodiment, the customized schedule isgenerated by developing a user-specific model using various sources ofuser-specific information which may include, but is not limited to: (1)user input submitted by the user in response to the initial userpreference questions presented during registration, (2) usage logscorresponding to the specific user's tracked consumption of mediacontent, (3) the personalized schedule and program guide (pEPG)interactivity logs, and 4) contextual factors such as external eventsand social group affiliations which may affect the user's consumptionbehavior.

For a given candidate unit content—the user model is then applied to theunit and used to predict the user's interest level in the candidateunit. The units may be internally rated and ranked, and units with arelatively high level of predicted user interest may be displayed in thepersonalized schedule and program guide and presented to the user atstep 205. User behavior, e.g., user interaction with the program guideis also tracked for the duration of a user's consumption of mediacontent at step 207. This user interaction is subsequently used torefine subsequent generations of customized schedules and programguides. In still further embodiments, the pEPG may be arranged accordingto one or more general content topics. In one example, the contenttopics may include Movies, Sports, Entertainment, Education, and News.The units of content determined to be of relatively high predicted userinterest may be distributed among each of the topics in the presentationof the pEPG, according to the meta-data corresponding to the units ofcontent.

As the source of the content can be linear content, recorded content,VOD, streamed-Internet-content, etc., the way the content in the programguide is presented to the user is influenced by the content meta-dataand the nature of the content. For example, linear content typically hasa fixed schedule whereas VOD can be watched at anytime. Other factorswhich may influence how and what content is recommended may beextraordinary and infrequent events such as the Olympics, unscheduledevents such as natural disasters (and ensuing news coverages), etc., aswell as the interests of people in social groups that the user isassociated with.

According to further embodiments, usage logging and access to agenerated customized schedule may require the identity of the user to beverified by the system, in order to access usage history logs and/orpre-submitted user input corresponding to the particular user, forexample. In one embodiment, the identity of the user is verified througha personal, remote computing device or through a graphical userinterface displayed on a display device. For registered users, verifyingthe identity may consist of simply entering authentication information(e.g., a username or account id and a password) corresponding to theuser's pre-established identity and/or account through a remotecomputing device, for example. A new user however, may be requested toregister with the system (e.g., submitting identification information),and, once registered, may be prompted to an initial set of preferencequestions that ask the user about the user's personal choices, interestsand other general questions later used to develop the data model used togenerate the customized schedule of recommended viewing options for theuser. A user who opts to skip answering all or portions of the questionsmay have default answers inferred for the user based on informationknown in the system corresponding to the user. For example, the user'sgeographic location, age, gender, ethnicity, etc., may be registered andstored during a previous registration step. The expressed preferences ofother user's with similar or identical personal information may bereferenced and used as a default inferred answer for the user. Once theuser's identity is established, recording of the user's activity (e.g.,tracking of the user's usage of the remote device 101 and contentwatched in the display device 103) begins.

With reference now to FIG. 3, a flowchart of a process 300 fordynamically generating a schedule of recommended available media contentis depicted, in accordance with embodiments of the present disclosure.Steps 301-307 describe exemplary steps comprising the process 300depicted in FIG. 3 in accordance with the various embodiments hereindescribed. In one embodiment, process 300 is performed during step 207of process 200 described above.

At step 301, pre-entered user input (if available) is referenced fromthe user's viewing profile. According to some embodiments, wherepre-entered user input is not immediately available or when the useropts against supplying user preference input, a schedule of recommendedavailable media content may still be generated for the user by theprocess 300 by omitting this step and proceeding directly to step 303.Alternatively, user preferences may be automatically attributed to theuser's viewing profile according to user-specific information whichcorresponds to the preferences of other similar users, as describedabove with respect to FIG. 2. According to these embodiments, thepreferences for the users may be subsequently updated at any time basedon supplied user input. According to an embodiment, to obtain the userinput, the user is prompted to respond to some general questions (in theform of a questionnaire) about the user's preferences for certaintopics. These topics may include, for example, real world activities andobjects. According to further embodiments, other questionnaires may bepresented to the user for user input periodically to update the user'sprofile with current information provided by the user. The user inputcorresponding to the questionnaire(s) is subsequently referenced at step301 and used to generate the user's viewing profile by mapping theuser's preferences to a fixed set of abstract classifications, with aset of preference weights that indicates a measure of the preference ofthe user for a particular abstract classification. A list of abstractclassifications that indicate the user's preference for movies mayinclude, for example, “genre,” “cast,” “director,” “language,” “rating,”etc. Each classification can have one or more corresponding attributesfrom a finite set of attributes indicating the preference of the userfor those attributes. For example, the abstract classification “genre”can take one or more values that include (but are not limited to):“comedy,” “action,” “romance,” “horror,” sci-fi,” etc.

According to some embodiments, each unit of media content has acorresponding one or more instances of meta-data which specificallycorrelate to similar classifications. For example, an unit of a mediacontent (such as a movie) may include the name of the director and oneor more starring actors. According to further embodiments, the meta-dataof an unit of media content corresponds specifically to one or moreabstract classifications and/or one or more attributes, allowing an unitof media content to be mapped to the user's preferences in the user'sprofile. If a specific attribute or abstract classification isrepeatedly encountered, its corresponding weight will increasesignificantly and this can influence the user's interest in otherattributes. Additional considerations that may influence a user'sviewing profile can include the times of day and days of week of loggedusage activity. Any repeatedly encountered variables or attributes ofconsumed media content may also be considered, according to variousembodiments. The user's interest in a candidate unit of media contentmay thereafter be predicted by finding the similarity of the meta-dataof the candidate unit of media content to the user profile over allclassifications, and the weight of the user's preference in thecorresponding applications. In one embodiment, the user's interest in aunit of candidate content that is calculated from the user's pre-entered(or pre-supplied) input may be expressed as a value (e.g., a userpreference value) that accounts for how many classifications orattributes of a unit of available media content (given by its meta-data)match the classifications or attributes in the user's profile, and themagnitude (weight) of the user's preference for those classifications orattributes. In further embodiments, the weight assigned to an abstractclassification or attribute may be disproportionately allocated so as toweight recent activity more heavily than less recent activity. In otherwords, the weight assigned indicating a user's interest in an abstractclassification or attribute may decay over time within the system. Inthis way, the interest of a user—expressed as a value—based on theuser's preferences in certain abstract classifications and attributesmay be used as input to model the user's viewing profile.

According to still further embodiments, the user's viewing profile canalso influenced by current contextual information corresponding to theuser. One source of contextual information is external events.Generally, a user's viewing choices are greatly influenced by the events(globally and locally) that are happening to or around the user. Forexample, when an international event such as the Olympics is going on,most people with an interest in sports tend to watch the Olympics ontelevision. Even if a user's specific interest in sports may not beknown, it is highly likely that the user will choose to watch theOlympics at some point. Naturally, the recommendation for a sports fanor in the sports category of a user's pEPG may be highly influenced byan external and/or extraordinary events such as the Olympics.

To evaluate the influence of external events on user interest, recentnews articles are collected. Sources for these news articles maycomprise, for example, Internet news websites, or other wired news feedservices. The news articles collected may be further classified intocorresponding categories and analyzed to determine a level of coverage(e.g., a frequency of reporting or occurrence in news and social mediasites). These categories may include, for example, sports,entertainment, politics, science/technology and health. Next, keywordsfrom the news articles are extracted, especially from the news storiesthat have high coverage. The keywords extracted may consist mostly ofpeople's names, events, organizations, places, etc., for example

Since candidate units of media content are described by a set ofabstract classifications, which in turn may include a set of attributes,which are themselves the equivalent keywords, this allows the mapping ofcurrent events to units of media content by finding matches and/orsimilarities between keywords. Other methods to obtain external orcurrent events may include determining trending topics in news websites,social networks, social interest aggregating websites and applications,or those specified by a user or operator of the system—typically withinspecified periods of time (e.g., breaking news bulletins). Once it isdetermined that some external events have a high similarity withcontents, the weighted interest of the user in the abstractclassifications corresponding to the units of available media contentmay be updated based on the popularity of the external event measured asa function of the coverage of the event, which influences the weight onthe keywords. This may also be seen as one component of therecommendation initialization strategy with a stronger influence whenthe user registers without answering any questions.

Similar to external events, interests of a social group affiliated withthe user can affect the user's viewing preferences. A user's viewingchoices, for example, are greatly influenced by the viewing decisionsand habits of the user's friends, relatives, and like-mindedindividuals, each a social group in which the user belongs andcollectively, the user's social network. Even more specifically, asocial group can refer to:

-   -   A group of people with whom the user is presently consuming        television. Namely, the exact group of people who are        geographically and proximally located or consuming television        together. In general, influence of this group is more        circumstantial.    -   A group of people who know each other (and the user) and        frequently communicate and share information with each other,        namely, friends on social media. Generally, influence of this        group is more significant, particularly depending on the degree        of communication activity. The effect is spread across multiple        categories of content.    -   One or more people with extraordinary influence on the user's        viewing interests. These people may include celebrities or other        notable people with whom the user has demonstrated an        association (even if unilaterally).    -   Groups of people who come together with a common interest. e.g.,        special interest groups like a group of movie lovers, soccer        enthusiasts etc. In general, the influence of this group is more        localized/focused on items of common interest.    -   Members of a household who generally watch television together,        namely, family members. The influence of this group could be        circumstantial and affect specific classifications only.    -   A group of people who have a common sense of belonging, e.g.,        for a community and country. Influence of this group affects        areas where the common sense of belonging is involved.

Depending on the group(s) the user belongs to, the popularity of acontent with respect to the user is rated. The process of rating acontent based on the user's group affiliation(s) is similar to that ofexternal events, except that ratings of the content is affected by thepreferred classifications of other users instead of a list of keywordsfrom external events. Thus, for example, the contextual influenceawarded to a social group interest may be derived by determining whichsocial groups are affiliated with the user, calculating the interestlevel of the social groups in units of available media content,weighting the social groups by the estimated level of influence on theuser, and rating the units of media content by applying the weightedinfluence of a social group interest to the rating of an unit of themedia content, or by adding the weighted interest of correspondingsocial groups to the user's preference value derived for a unit ofavailable content. Since social groups can be explicit (when the userhas indicated his belonging to a group explicitly) and implicit(grouping users based on age, gender, social status, ethnicity etc),interests of implicit social groups may also be used for initializingpreference weights of abstract classifications. According to oneembodiment, this may have a stronger influence on the initialization ofcontent recommendations when the user has not answered the questions onpreference for abstract classifications while registering.

At step 303, the user's history of consumed media content and userinterface usage log are analyzed to observe patterns and recordstatistics, which are subsequently mapped to units of available mediacontent and used to predict the user's interest in the units ofavailable media content. The system logs and stores every interactionand behavior the user has through the user interface, including whatchannel/show the user is watching, which source the user is watching(e.g., cable, internet, etc), what show the user recorded, the user'schannel surfing or browsing behavior, likes/dislikes etc. In oneembodiment, the log of the remote computing device is analyzed todistill user behavior among a set of three distinct acts—1) a user'sselection of an unit of media content, 2) a user's continuousconsumption of the media content once selected, and 3) the user'sdiscontinuation of consumption of the media content. According to anembodiment, a user's interest in consumed media content is determined byevaluating the following factors:

-   -   1. The reasons behind why the user selected the content.    -   2. The reasons behind why the user continued to watch the        content for time ‘t’.    -   3. The reasons behind why the user discontinued watching the        unit of content.

According to further embodiments, these factors may be inferred byconsidering indications of a user's behavior. Indications about theuser's interest (or disinterest) in a content could be either explicitor implicit. Explicit indications are those that are made clear eitherby the user (e.g., manually indicating like or dislike etc), or inferredbased on known behavior traits. On the other hand, implicit indicationsare those which are inferred from the usage logs based on statistics,rules or other data mining techniques. According to some embodiments,implicit indications are weighed less in terms of their influence onuser interest. However, most of the information available in logs ismainly implicit, which makes their contribution significant.

The probability of each user behavior, that is, selection, consumption,and discontinuation, for an unit of media content watched by the usermay be determined by considering the combined weights of each indicationcorresponding to the behavior for that unit. Relatively many and/orheavily weighted indications would therefore suggest a high probabilityof a user behavior. Once user behavior for consumed content is modeled,the behavior may be mapped to related units of available media contentto predict user interest (e.g., as a value). For example, content thatshares similar meta-data characteristics, content that was recorded, thesame unit of content that was viewed in the past on multiple occasions,etc. may be mapped together. In this way, the interest of a user in acandidate content based on the user's historical consumption and userbehavior history may also be used as input to model the user's viewingprofile. In one embodiment, the interest of the user in a candidatecontent based on the user's historical consumption and behavior historymay be expressed as a value—such as a user behavior value—indicative ofthe user's predicted interest in a unit of candidate media content.

As the user uses the system by selecting and watching content, theuser's profile will be updated according to the proximity of thecontent's meta-data with the user's present profile. Once the system 100begins to recommend some content, the system is then able to benefitfrom the feedback that the user provides while using the recommendedschedule and personalized electronic program guide (pEPG) correspondingto the user. For example, an exemplary pEPG interface may allow the userto view and select, delete and add VOD or cable/satellite show to atime-line based electronic program guide. According to such animplementation, the operations performed by the user which are recordedmay include, (1) Select, (2) Switch to another content, (3) Delete acontent, (4) Add a content, (5) Like, (6) Dislike, (7) Share a content,and (8) View the unit of media content's trailer/synopsis. Theseoperations applied to content actually viewed affect the user profilewhich is updated for every such interaction. In this way the interest ofa user in a candidate content based on the user's interactions with thepEPG framework is also used as input to model the user's viewingprofile. In one embodiment, the interest of the user in a candidatecontent based on the user's interactions with the pEPG framework may beexpressed as a value—such as a user-initiated operationsvalue—indicative of the user's predicted interest in a unit of candidatemedia content.

At step 305, the user's viewing profile is modeled from the pre-entereduser input (if available) entered at step 301, and the user log analysisperformed at step 303 to determine the probability of a high level ofuser interest in units of available media content. Total User Interestin a candidate unit of media content may thus be computed by combiningthe user's computed interest in abstract classifications and/orattributes which correspond to meta-data of the candidate unit (asperformed in step 301), the interest of a user in a candidate contentbased on the user's historical consumption and user behavior history),and the interest of a user in a candidate content based on the user'sprevious interactions with the pEPG. In one embodiment, the valuescorresponding to each of these user interests may be combined for atotal user interest score. For example, the user behavior value, userpreference value (which may be influenced by contextual influence valuesof external events and/or associated social groups) and user operationsvalue calculated from the analyses performed at steps 301 and 303 for aunit of available media content may be combined as a raw user interestscore for the unit of available media content.

The units of media content with meta-data most proximate to thepreferred abstract classifications and attributes, and with the highestlevel of predicted user interest based on analyzed usage logs (e.g., thehighest rated unit of available content) are selected for display in thepEPG. Proximity of media content to a user profile may be determinedusing a nearest neighbor (NN) approach and taking the top-N (as selectedby the user or formatted for display) results. The nearest neighbordistance measure is an adaptive weighting measure that is a function ofthe users' weighted interest in abstract classifications and hispreferred abstract classifications. The top-N results may be thereafterdisplayed (e.g., as a list) in the pEPG generated in the user's remotedevice at step 307. Alternately, instead of displaying a list ofresults, the content may be arranged as an information stream or“content feed” to the user. According to such an implementation, theuser may indicate “next” to go from one unit of content in the contentfeed to the next. In still further embodiments, conventional “channelup/down” functionality may be adapted to instead allow a user is able tonavigate between “previous” and “next” units of content, therebyprompting further user input comprising user selections which may beused to further update the user's profile. As the updated profile of auser is greatly influenced by his present profile at any time instant,this provides the ability to dynamically generate timely schedules andprogram guides to reflect the user's current preferences and tastes.

With reference now to FIG. 4, a data flow diagram 400 of a process forperforming usage log analysis in a system is depicted, in accordancewith embodiments of the present disclosure. At time 1), a usage log fora user's viewing history is received. The user's viewing history may beobtained by, for example, referencing the user's usage logs in a datarepository corresponding to the system. At time 2), the user's usageviewing history may be filtered to remove content that the user did notconsume (i.e., watch) below a threshold duration. By filtering thecontent below a certain threshold, insignificant noise due to user“zapping” or “channel surfing” may be eliminated to reduce or removeinaccurate effects to the user's recorded behavior. At time 3), thefiltered list of consumed content is compiled, and reasons (e.g., eventsor indications) corresponding to identified user behavior is extractedat time 4). As depicted, user behavior is identified within the systemas either select, watch, or leave, and contributing reasons may be mined(at time 5) from events which occurred during each of the user behaviorsfor any unit consumed. At time 6), each of the reasons contributing to auser behavior may be weighted according to nature of the particularreason, event, or indication. For example, explicit reasons may beafforded greater weight than implicit reasons. These weights may beapplied to available media content to determine a unit of content'srating, representing a user's predicted level of interest, as describedabove with respect to FIG. 3, for example.

FIG. 5 depicts a list 500 of exemplary reasons contributing to loggeduser behavior, in accordance with embodiments of the present disclosure.These reasons (which comprise events or indications) are described ingreater detail above with respect to step 305 of FIG. 3, and may beextracted from usage history for a unit of media content as describedabove with respect to FIG. 4. As presented in list 500, each of theidentified user behaviors (e.g., Select, Watch, and Leave) has acorresponding plurality of reasons which, if occurring during theperformance of any of the user behaviors, may have contributed to theuser's performance of the particular behavior. Cross-referencing auser's individual act with a user's usage history then, may create orreinforce an association between the act and a particular reason. Forexample, a user who selects to watch a program that the user's usagehistory indicates the user consistently watches the same, regularlyscheduled program at its scheduled day and time (e.g., S4) may indicate(and corroborate) the reason for the user's selection. During a usagelog analysis process, past user behavior and usage history may befiltered and extracted to determine reasons for a particular userbehavior which occurred during instances of the identified userbehavior. As shown in FIG. 5, each reason is further indicated as eitherExplicit or Implicit. Certain embodiments may allocate greater weight toexplicit reasons as opposed to implicit reasons. Accordingly, thesereasons may be mapped or applied to units of available content, and thecorresponding weights of the reasons may be factored when assigning avalue or rating for a particular unit of programming which representsthe user's predicted level of interest.

FIG. 6 depicts a sample questionnaire 600 to query a user foruser-input, in accordance with embodiments of the present disclosure. Asdepicted in FIG. 6, a user may be prompted with a list of questions, theuser's responses of which correspond to classifications and may be usedto infer a user's interest to content with meta-data similar or equal tothose classifications. This way, a user's indicated, explicit interestsor preferences in other activities may be used to model the user'slikely preference of media content. User response to a list ofquestions, such as sample questionnaire 600, may be used in the systemand referenced during the creation of the user model.

Exemplary Media System

With reference now to FIG. 7, an exemplary system 700 for generating apersonalized media content schedule for a user is depicted in accordancewith embodiments of the present disclosure. According to an embodiment,system 700 includes a display device 703, and a plurality of mediacontent sources (e.g., content sources 705, 707, 709, and 711)communicatively coupled to the display device. The system 700 may beimplemented to develop a recommendation system that can generate arecommended guide and schedule for viewing programs across multiplesources of content (705, 707, 709, 711) on the display device 703 basedon knowledge of a user's viewing interests. The user's viewing interestsare modeled based on analyzing the user's television usage logs,responses to a personalized questionnaire, and content meta-data. Thedisplay device 703 may be implemented as, for example, a television,monitor, or projector screen, etc. The media content sources mayinclude, but are not limited to, a cable or satellite receiver 705, aset top box 707, a home media center 709, and a video game console 711.

According to one embodiment, the media content sources may be coupledto, and configured to provide content from a plurality of media contentproviders, such as a cable television service, a satellite televisionservice (both including live, recorded, scheduled, and VOD content), andVOD content over the Internet. According to an embodiment, the displaydevice 703 is operable to display the content provided by the multiplesources (705, 707, 709, 711) connected to it—such as cable and satellitetelevision, personal video recorders, Internet etc. Other contentsuitable for display within the display device may include downloadedelectronic copies of content stored both locally (e.g., the same machineor local media storage device) and remotely (e.g., on a remote or cloudserver).

As depicted, FIG. 7 further includes a remote or personal computingdevice 701, which may be implemented as a tablet computing device (asshown), laptop, smart-phone, or a remote control of the television orcontent source. The display of a personalized media content schedulegenerated by the system 700 may be displayed to the user in a graphicaluser interface of the remote computing device 701. In still furtherembodiments, multiple, simultaneous viewers of the same display device703 may each have a different remote computing device 701, eachgenerating and displaying an individually personalized media contentschedule for the corresponding user. The system 700 allows every user tocontrol the television and connected devices with a software applicationexecuting on the user's personal device 701.

Alternately, multiple viewers may share a single remote or input device701 capable of generating a personalized media content schedule for eachuser. To access functionality of the system 700, the remote 701 mayrequire the user to verify the user's identity, such as by entering anaccount name and password or establishing a new identity with thoseparameters, or to select pre-established accounts and/or identities. Forexample, a remote device 701 may be configured with a plurality ofselectable user identities. To begin tracking of user viewing historyand to enable the generation of personalized content schedules, a usersimply acknowledges an identity with the remote device 701 throughsoftware (accessible through a graphical user interface, or via useractuation of a physical button, for example).

Every user's television usage activity is logged by the system 700.Examples of usage activity logged by the system for each user mayinclude the specific units of content consumed during a usage session,the corresponding durations of each unit consumed, the order in whichthe units of content were consumed (viewed). According to oneembodiment, a set of recommended programs are identified which isrecommended to the user in a schedule. In one embodiment, the scheduleis implemented as one or more time-lines. This recommendation—referredto herein as a personalized electronic program guide (pEPG)—may bedisplayed in a graphical user interface, such as a display of the remotedevice 701. Alternately, the pEPG may also be displayed in the displaydevice 703, accessible on-demand to the user. The user's responses andinteractions to this recommendation is also logged (e.g., the user canchose to delete or add shows to the time-lines). By allowing each viewerthe usage of a remote computing device 701 during media consumption on atelevision, additional usage data (e.g., media consumption history andusage behavior history) may be collected, analyzed, and the schedule maybe updated dynamically for each user.

Logging of the usage activity, analysis of logged usage activity, andthe generation of the pEPG may be performed together or separately byany one of a plurality of software instances executing in the devicescomprising system 700, in accordance with various embodiments. Forexample, any or all of the functionality may be performed by softwareexecuting in the remote device, the display device, a local computingdevice or media content provider (e.g., a cable box, home media server),or even a remote computing device (remote server) communicably coupledto any of the above devices.

Exemplary User Interface

With reference now to FIG. 8, a first exemplary graphical user interface800 of a dynamically generated schedule of recommended available mediacontent is depicted, in accordance with embodiments of the presentdisclosure. In one configuration, user interface 800 is generated anddisplayed in the remote computing device 701 of a system for generatinga personalized schedule of recommended available media content, such asthe system 700 described above with respect to FIG. 7. In furtherembodiments, the user interface 800 may be displayed directly in thedisplay device 703, but operated by the remote computing device 401. Inone embodiment, graphical user interface 800 represents the interfacedepicted when a display device is turned off, and the display of thepEPG is activated.

As presented in FIG. 8, user interface 800 includes functionality foroperating a display device (e.g., display device control panel 801),functionality to preview selected or recommended content in a scheduledtimeline (e.g., preview display area 803), a plurality of contentdisplays 809, 813 which provide information on selected or recommendedcontent, and a plurality of user-input fields which may be graphicallyimplemented as, for example buttons, fields, menus, knobs, etc. (e.g.,user input menu 807; window button 805, and user preference indicators811). In one embodiment, operation of a display device may be performedin the control panel 801. As depicted, control panel 801 includesbuttons for numeric input (e.g., during channel selection), buttons forincrementing and decrementing the channel selection and volume, andother pre-programmed functionality, such as “mute,” and “home.” In oneembodiment, the preview display area 803 is operable to display aselected unit or portion of an unit of media content. Content displays809 and 813 may display specific information corresponding to theselected unit and/or provide additional external context. For example,content display 809 may display information or an introduction to theunit of media content being previewed in display area 803. Meanwhile,content display 813 may display news headlines corresponding to currentevents.

The user interface may include functionality to receive user input. Forexample, User preference indicators 811 may be used to indicate a user'spreference for the selected unit of media content. As shown, userpreference indicators 811 include a plurality of buttons (e.g., “Like,”“Dislike,” Share”), and an information field (e.g., “Current Choice”). Auser is able to indicate the user's appreciation and/or interest in theselected unit of media content by actuating any of the plurality ofbuttons. User actuation of buttons are logged and used to update themodel of the user's viewing profile. For example, user actuation of the“Like” button may increase the weight of the abstract classificationand/or specific attributes of the selected unit of content, as well asincrease the weight of certain indicators. Conversely, user actuation ofthe “Dislike” button may have the opposite effect. User actuation of the“Share” button may publish, via one or more connected social networkapplications the user's choice. Once either the “Like” or “Dislike”buttons are actuated, the user's most recent choice may be presented inthe “Current Choice” information field. In alternate embodiments, theuser may be able to toggle the display of content in the user interface800. The display of content in the user interface 800 may display thecontent of the pEPG, as described above.

By using any of the systems or methods provided above, a customizedschedule of recommended content from multiple content sources may bedynamically generated for and presented to a user based on a data modelof the user's viewing habits and usage history, explicit input, andcontent meta-data to assist the user in choosing between a plurality ofviewing options. Although the subject matter has been described inlanguage specific to structural features and/or processological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims.

What is claimed is:
 1. A method for providing a personalized mediacontent schedule for a user, the method comprising: determining, in aprocessor of a computing device communicatively coupled to a displaydevice, a plurality of units of available media content from a pluralityof content sources; dynamically generating a schedule of recommendedunits of available media content from the plurality of units ofavailable media content, the schedule being customized for a user of thedisplay device; displaying to the user a graphical display of theschedule of recommended units of available media content, the displaycomprising an ordered listing of a plurality of recommended units fromthe schedule of recommended units of available media content arrangedaccording to a relative predicted level of user interest in each of theplurality of units of available media content; and tracking userinteraction with the schedule of recommended available units of mediacontent, wherein the schedule of recommended available units of mediacontent is customized for the user by generating a viewing profile forthe user based at least on a plurality of past instances of userbehavior corresponding to units of media content previously consumed bythe user and mapped to the units of available media content, furtherwherein the viewing profile is used to predict the relative level ofuser interest in each of the plurality of units of the available mediacontent by assigning weighted values to a plurality of extracted reasonscontributing to the past instance of user behavior, and identifying apast instance of user behavior with the greatest weighted sum of values.2. The method according to claim 1, wherein generating the viewingprofile of the user comprises modeling a viewing profile of the userbased on a referenced user input, a media content consumption history,and a plurality of previous user interactions with a plurality ofpreviously generated schedules of recommended available media content.3. The method according to claim 2, wherein modeling a viewing profilebased on the media content consumption history comprises: tracking userbehavior patterns exhibited by the user corresponding to the units ofmedia content previously consumed by the user; and determining userbehavior values corresponding to the plurality of units of availablemedia content based on a comparison between the plurality of units ofavailable media content with the units of media content previouslyconsumed by the user.
 4. The method according to claim 3, whereindetermining a user behavior value corresponding to a unit of availablemedia content comprises: mapping the unit of available media content tothe units of media content consumed by the user corresponding to theplurality of past instances of user behavior; predicting an instance ofuser behavior corresponding to the unit of available media content; andcalculating the user behavior value for the unit of available mediacontent based on the predicted instance of user behavior for the unit ofavailable media content.
 5. The method according to claim 4, whereinmapping the unit of available media content to the units of mediacontent consumed by the user comprises: filtering the user behaviorpatterns to identify a plurality of past instances of user behaviorperformed by the user; extracting reasons contributing to the pluralityof past instances of user behavior from the units of media contentcorresponding to the plurality of past instances of user behavior; andmapping the reasons for the plurality of past instances of user behaviorto the plurality of units of available media content.
 6. The methodaccording to claim 1 wherein the plurality of past instances of userbehavior comprise: past instances of a user selection of a unit of mediacontent; past instances of a user consumption of a unit of mediacontent; and past instances of a user discontinuation of a userconsumption of a unit of media content.
 7. The method according to claim6, wherein the reasons for the plurality of past instances of userbehavior comprise: a) a group of reasons corresponding to a userselection of the unit of media content; b) a group of reasonscorresponding to a user consumption of the unit of media content; and c)a group of reasons corresponding to the user discontinuation of a userconsumption of the unit of media content.
 8. The method according toclaim 7, wherein each group of reasons a), b), and c) comprises aplurality of indications of a user interest in a unit of media content,the plurality of indications comprising either an implicit indication oran explicit indication.
 9. The method according to claim 1 whereinidentifying a past instance of user behavior with the greatest weightedsum of values comprises: calculating a weighted sum of values for eachpast instance of user behavior mapped to the unit of available mediacontent from the weighted values of the reasons corresponding to thepast instance of user behavior; and comparing the weighted sum of valuesof each past instance of user behavior corresponding to the unit ofavailable media content.
 10. The method according to claim 2, whereinmodeling a viewing profile of the user based on the referenced userinput comprises referencing pre-entered user input corresponding topreferences of the user in a plurality of abstract classifications. 11.The method according to claim 10, wherein the plurality of abstractclassifications are comprised from a fixed set of abstractclassifications, each abstract classification from the fixed set ofabstract classifications comprising a plurality of attributes from afixed set of attributes.
 12. The method according to claim 11, whereineach abstract classification from the fixed set of abstractclassifications is assigned a weighted value.
 13. The method accordingto claim 12, wherein modeling a viewing profile of the user based on thereferenced user input comprises: mapping the plurality of units ofavailable media content to the fixed set of abstract classifications;and determining a plurality of user preference values for the units ofavailable media content based on the weighted values of the abstractclassifications mapped to the units of available media content.
 14. Themethod according to claim 13, wherein modeling the viewing profile isfurther based on determining contextual influence values correspondingto the units of available media content, the contextual influence valuesbeing comprised from a group consisting of: external event values andsocial group interest values.
 15. The method according to claim 14,wherein determining contextual influence values of external eventscorresponding to the units of available media content comprises:collecting a plurality of external events occurring within a window oftime; categorizing the plurality of external events among a plurality ofpre-set categories; deriving a plurality of weighted values of theplurality of external events based on predicted levels of interest ofthe user in the plurality of external events; mapping the plurality ofexternal events to units of available media content by comparing aplurality of abstract classifications corresponding to the units ofavailable media content to the pre-set categories corresponding to theplurality of external events; and determining a contextual influencevalue of external events for the units of available media content basedon the derived plurality of weighted values corresponding to theplurality of external events mapped to the plurality of external events.16. The method according to claim 14, wherein determining contextualinfluence values from social group interest values for the plurality ofunits of available media content comprises: determining a plurality ofsocial groups corresponding to the user; calculating an interest levelof each of the plurality of social groups in the units of availablemedia content; deriving a plurality of weighted values of the pluralityof social groups corresponding to the user according to a level ofinfluence on the user of each of the plurality of social groups; anddetermining a contextual influence value of social group in events forthe units of available media content based on the interest levels of theplurality of social groups in the units of available media content andthe levels of influence on the user of the plurality of social groups.17. The method according to claim 2, wherein modeling a viewing profileof the user based on the plurality of previous user interactions with aplurality of previously generated schedules of recommended availablemedia content comprises: tracking user interactions with the pluralityof previously generated schedules of recommended available mediacontent; filtering the user interactions to identify a plurality ofuser-initiated operations and units of media content consumed by theuser corresponding to the plurality of user-initiated operations;assigning weighted values to the plurality of user-initiated operations;mapping the units of available media content to the units of mediacontent consumed by the user corresponding to the plurality ofuser-initiated operations; and calculating a plurality of useroperations values for the units of available media content based on theweighted values of the plurality of user-initiated operations mapped tothe units of available media content.
 18. The method according to claim17, wherein the predicted level of user interest corresponding to a unitof available media content comprises an aggregate value comprising acalculated user behavior value, a determined user preference value, anda calculated user operations value corresponding to the unit ofavailable media content.
 19. The method according to claims 18, whereinthe aggregate value further comprising the contextual influence valuescorresponding to the unit of available media content.
 20. The methodaccording to claim 12, wherein the value attributed to an abstractclassification corresponding to recent user activity is weighted moreheavily than the value attributed to an abstraction classificationcorresponding to less recent user activity.
 21. The method according toclaim 12, wherein the value attributed to an abstract classificationcorresponding to frequently repeated user activity is weighted moreheavily than the value attributed to an abstraction classificationcorresponding to less frequently repeated user activity.
 22. A systemfor providing a personalized media content schedule for a user, thesystem comprising: a display device; a receiver, communicatively coupledto the display device, and configured to receive a media content from aplurality of content providers for the display device; a control devicecorresponding to a user and communicatively coupled to the displaydevice and operable to track an operation of the display device, acomputing device, communicatively coupled to the control device andconfigured to dynamically generate a customized schedule of recommendedmedia content from the plurality of content providers for the user froma plurality of units of available media content from the plurality ofcontent providers, wherein the schedule of recommended media contentcomprises a plurality of units of recommended media content and iscustomized for the user using a viewing profile model generated for theuser, based at least on a plurality of past instances of user behaviorcorresponding to units of media content previously consumed by the userand mapped to the units of available media content, further wherein, theviewing profile is used to predict the relative level of user interestin each of the plurality of units of available media content byassigning weighted values to a plurality of extracted reasonscontributing to the past instance of user behavior, and identifying apast instance of user behavior with the greatest weighted sum of value.23. The system according to claim 22, wherein the available mediacontent from the plurality of content sources is comprised from a groupof media content including: cable television content; satellitetelevision content; Video on Demand services; recorded televisioncontent; live television content; stored, electronic user-generatedcontent; and digital content streamed over the Internet.
 24. The systemaccording to claim 22, wherein the computing device is further operableto: query the user for user input corresponding to preferences of theuser in consuming multimedia content; store the user input correspondingto preferences of the user in consuming media content, wherein the userinput corresponding to preferences of the user in consuming multimediacontent is referenced to generate the viewing profile model of the user;and determine a plurality of user preference values corresponding to theplurality of units of available media based on a plurality of abstractclassifications mapped to the units of available media content andcorresponding to preferences of the user in consuming media content. 25.The system according to claim 22, wherein the computing device isfurther operable to: track user behavior patterns exhibited by the usercorresponding to units of media content consumed by the user; anddetermine a plurality of user behavior values corresponding to theplurality of units of available media content based on a comparisonbetween the plurality of units of available media content with the unitsof media content consumed by the user.
 26. The system according to claim22, wherein the computing device is further operable to: track userinteractions with the previously generated schedules of recommendedavailable media content; determine a plurality of units of user-consumedcontent corresponding to the user interactions with the previouslygenerated schedules of recommended available media content; map theunits of available media content with the plurality of units ofuser-consumed content corresponding to the user interactions with thepreviously generated schedules of recommended available media content;and calculate a plurality of user operations values corresponding to theunits of available media content.
 27. The system according to claim 22,wherein the computing device is further operable to: connect to aplurality of news sources; collect a plurality of current events fromthe plurality of news sources; categorize the plurality of current eventaccording to a plurality of pre-set categories; determine a plurality ofweighted values for the plurality of current events; map the pluralityof current events to the units of available media content; and determinea plurality of contextual influence values corresponding to theplurality of units of available media based on the weighted values ofthe plurality of current events mapped to the units of available mediacontent.
 28. The system according to claim 22, wherein the computingdevice is further operable to: connect to a social group hub; determinea plurality of social groups corresponding to the user; determine aplurality of levels of influence on the user of the plurality of socialgroups corresponding to the user; determine a level of user interest ofthe plurality of social groups corresponding to the user on the units ofavailable media content; and determine a plurality of contextualinfluence values corresponding to the plurality of units of availablemedia based on the plurality of levels of influence on the user of theplurality of social groups corresponding to the user and thecorresponding levels of interest in the units of available media contentof the plurality of social groups corresponding to the user.
 29. Thesystem according to claims 22, wherein the computing device is furtheroperable to calculate a level of user interest in each unit of availablemedia content by aggregating a plurality of user preference values, aplurality of user behavior values, a plurality of user operationsvalues, and a plurality of contextual influence values for the unit ofavailable media content.
 30. The system according to claim 22, whereinthe viewing profile model is generated based on a media contentconsumption history corresponding to the user, a referenced user input,and a plurality of previous user interactions with previously generatedschedules of recommended available media content schedule of recommendedavailable media content.
 31. A non-transitory computer readable mediumcontaining program instructions embodied therein for causing a computingsystem to implement the provision of a personalized media contentschedule for a user, the program instructions comprising, programinstructions to determine available media content from the plurality ofcontent providers; program instructions to dynamically generate aschedule of recommended available media content customized for a user ofthe display device; program instructions to display the schedule ofrecommended available media content as an ordered listing of a pluralityof recommended units from the schedule of recommended units of availablemedia content arranged according to a relative predicted level of userinterest in each of the plurality of units of available media content;and program instructions to track user interaction with the schedule ofrecommended available media content, wherein the schedule of recommendedavailable units of media content is customized for the user bygenerating a viewing profile for the user based at least on a pluralityof past instances of user behavior corresponding to units of mediacontent previously consumed by the user and mapped to the units ofavailable media content, further wherein the viewing profile is used topredict the relative level of user interest in each of the plurality ofunits of the available media content by assigning weighted values to aplurality of extracted reasons contributing to the past instance of userbehavior, and identifying a past instance of user behavior with thegreatest weighted sum of values.
 32. The non-transitory computerreadable medium according to claim 31 wherein the program instructionsto dynamically generate a schedule of recommended available mediacontent comprises: program instructions to model the viewing profile ofthe user based on a referenced user input and an analyzed media contentconsumption history to determine a probability of user interest in eachof a plurality of units of the available media content.